The sustainability of bridge infrastructure is becoming increasingly important due to rising environmental, economic, and social demands. However, most current assessment models remain fragmented, often overlooking the social pillar, underutilizing risk integration across the lifecycle, and failing to fully leverage digital tools such as Building Information Modeling (BIM) and Life Cycle Sustainability Assessment (LCSA), resulting in incomplete sustainability evaluations. This study addresses these limitations by introducing a practical and adaptable model that integrates BIM, LCSA, and expert-driven risk prioritization. Five Hungarian bridge projects were modeled using Tekla Structures and analyzed in OpenLCA to quantify environmental, economic, and social performance. A custom Sustainability Level Change (SLC) algorithm was developed to compare baseline scenarios (equal weighting) with risk-informed alternatives, simulating the impact of targeted improvements. The results demonstrated that prioritizing high-risk sustainability indicators leads to measurable lifecycle gains, typically achieving SLC improvements between +2% and +6%. In critical cases, targeted enhancement scenarios, applying 5% and 10% improvements to top-ranked, high-risk indicators, pushed gains up to +12%. Even underperforming bridges exhibited performance enhancements when targeted actions were applied. The proposed framework is robust, standards-aligned, and methodologically adaptable to various bridge types and lifecycle phases through its data-driven architecture. It empowers infrastructure stakeholders to make more informed, risk-aware, and data-driven sustainability decisions, advancing best practices in bridge planning and evaluation. Compared to earlier tools that overlook risk dynamics and offer limited lifecycle coverage, this framework provides a more comprehensive, actionable, and multi-dimensional approach.

The accurate determination of the rheological properties of road bitumen types is essential for the reliable prediction of long-term pavement behaviour. At 60 °C, dynamic viscosity is a key rheological parameter characterising the shear-dependent viscoelastic behaviour of bitumen in the temperature range relevant to in-service pavement loading. This study aims to compare different viscosity determination methods—approximations, capillary viscosity, Brookfield measurement and complex viscosity determined by a dynamic shear rheometer (DSR)—and to analyse their relationships with each other in order to find the best method for bitumen classification. Furthermore, the European and Australian bitumen classification standards are compared in terms of dynamic viscosity and penetration, according to which Australian bitumen types show more stable results, as the CV% is less than 10 percent. The study is based on the testing of Hungarian paving-grade bitumens (B50/70, B70/100) and Australian viscosity-graded bitumens (C170, C320), with the comparison of a total of 191 samples obtained from industrial production. The statistical evaluation of the results obtained with the different methods was based on Pearson correlation analysis and relative deviation analysis. The results indicate that the DSR measurement at 1.6 Hz shows the closest agreement with capillary viscosity, with a linear correlation coefficient of 0.95, and exhibits the strongest overall correlation with the other measurement approaches, whereas the Heukelom equation tends to overestimate the dynamic viscosity. The Brookfield method yielded higher viscosity values in all tests. The study highlights that the results of different measurement methods can only be compared under specific shear conditions, and a DSR-based approach can be more suitable for the introduction of a new European bitumen classification system.